Brand Mentions in LLMs for an International Retail Company: How Zeno Visibility Compared Mentions Across Multiple Models
Brand Mentions in LLMs for an…
Situation
An internationally operating trading company based in Germany, with 1,800 employees and sales in 14 countries, was facing a typical challenge in the transition from traditional search to generative search: the brand was well established in the market, but appeared in AI responses only sporadically. The company sells technical consumer and industrial products through wholesale, specialist retail, and selected e-commerce channels. Around 240,000 B2B and B2C inquiries are processed each year, with an increasing share coming from research-heavy buying journeys with longer decision cycles.
The marketing team was already tracking classic SEO metrics such as rankings, traffic, and conversions. What became clear, however, was that these metrics only partially reflected the new reality. In internal tests, competitors appeared more frequently than the company as the recommended brand in ChatGPT, Perplexity, Gemini, Claude, and Copilot, even though the company ranked ahead of competitors in organic search results for many core keywords. Brand presence was particularly inconsistent for information-driven prompts about product comparisons, use cases, and delivery availability.
In spring 2025, the team decided to implement structured AI Visibility Monitoring with Zeno Visibility in order to systematically measure its presence in LLMs and analyze the reasons behind fluctuating mentions.
Challenge
The central challenge was not lack of awareness, but lack of machine-readable authority. In some cases, the brand was correctly recognized by the models, but in others only indirectly through product categories or trade brands. In some answers, the company was not mentioned at all, even though the content was available on the website. In addition, different models prioritized different sources and answered the same query with different brands, attributes, or delivery arguments.
For the company, this had direct consequences: product inquiries from generative assistants were difficult to attribute, the impact of content investments remained unclear, and the team could not derive a reliable prioritization for GEO measures. Classic content reports showed traffic and rankings, but not whether the brand was being used by LLMs as a trusted source. That meant there was no basis for decisions around topic clusters, internal linking, schema markup, and content depth.
Solution
Zeno Visibility was used in two phases: first for research, then for the systematic expansion of semantic authority. In the first phase, the team configured a prompt catalog with 46 search scenarios mapped to the buyer journey: product comparisons, delivery availability, quality standards, industry applications, price-performance arguments, and competitor comparisons. These prompts were tested in parallel across ChatGPT, Gemini, Perplexity, Claude, and Copilot. Zeno Visibility’s research engine captured whether the brand was mentioned, in what context it appeared, which sources the models relied on, and how the Semantic Authority Score performed by topic.
The analysis revealed three patterns: first, brand presence was significantly higher for general industry questions than for specific use-case or comparison questions. Second, semantic connections between core products, use cases, and proof content such as case studies, FAQ pages, and technical explanations were often missing. Third, structured data and internal linking were inconsistently implemented on the most important landing pages.
Based on these insights, the team used the Authority System Builder from Zeno Visibility to create a complete authority system for each prioritized keyword cluster. This included new hub pages, explanatory blog posts, FAQ blocks, comparison pages, two industry-specific case studies, and supporting social posts. Zeno Visibility generated semantically connected content ready for the CMS in multiple formats and simultaneously provided Schema.org JSON-LD as well as a recommended internal linking structure. The content was published directly in WordPress and Contentful; the team exported additional components into HTML and Gutenberg formats.
The key was not sheer content volume, but machine-readable consistency. Each core product category received defined entities, clear synonyms, traceable evidence, and links to trustworthy proof points. The goal was not just to increase the company’s visibility in the models, but to establish it as a recommended source in the relevant answer patterns.
Results
After 12 weeks, a clearly measurable effect emerged. In the initial model tests, the brand was mentioned in 31% of relevant prompts; after the authority systems were relaunched, the mention rate increased to 57%. The gain was especially strong for comparison and decision-making queries: here, brand mentions rose from 18% to 49%. The Semantic Authority Score improved by an average of 34 points across the prioritized clusters.
Distribution across models also became more balanced. While initially only Perplexity and ChatGPT mentioned the brand regularly, Gemini, Claude, and Copilot also increased their mention frequency significantly after implementation. At the same time, AI-referred sessions increased by 38%, and the conversion rate of those visits was 19% above the average for all organic sessions. Particularly relevant for the team: content with Schema.org markup and tight internal linking achieved the strongest improvements in model mentions.
The ROI could be estimated conservatively. The investment in research, content execution, and technical structuring paid for itself after around six months through additional qualified leads and improved content efficiency. Above all, the team saved internal coordination time, because AI Visibility Monitoring no longer just exposed visibility issues, but also prioritized specific areas for action.
Lessons Learned
Summary
With Zeno Visibility, the international trading company was able to understand why the brand appeared inconsistently in LLMs and systematically address the root causes. Through parallel AI Visibility Monitoring, semantic content systems, and technical structuring, brand mentions, authority signals, and qualified AI referrals all increased measurably. The case study shows: anyone serious about GEO must not only measure visibility, but actively build authority.